UID:
almahu_9949641726302882
Umfang:
1 online resource (320 pages).
ISBN:
9788770228268
,
8770228264
,
9781000963977
,
1000963977
,
9781003440864
,
100344086X
,
9781000964011
,
1000964019
Serie:
River Publishers series in computing and information science and technology
Inhalt:
In the era of propelling traditional energy systems to evolve towards smart energy systems, systems, including power generation energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading. This book covers the applications of various big data analytics, artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies such as the Internet of Things, blockchain, etc. for smart home solutions, and smart city solutions in depth in the context of the modern power systems. Technical topics discussed in the book include: • Hybrid smart energy system technologies • Energy demand forecasting • Use of different protocols and communication in smart energy systems • Power quality and allied issues and mitigation using AI • Intelligent transportation • Virtual power plants • AI business models.
Anmerkung:
Preface xiii List of Figures xvii List of Tables xxi List of Contributors xxiii List of Abbreviations xxv 1 Innovation, Opportunities, and Ongoing Challenges of AI and IOE in the Power and Energy Sector 1 1.1 Introduction 2 1.2 Recent Advances in Smart Energy Industry's Adoption of Artificial Intelligence 4 1.3 Internet of Energy (IOE) 5 1.4 Artificial Intelligence and IOE Role Toward Carbon Neutrality 6 1.5 Motivation 8 1.5.1 IOE-digitalization 8 1.5.2 IOE-decentralization 9 1.5.3 IOE-decarbonization 10 1.5.4 IOE-electrification 10 1.6 AI Application and Solution 10 1.6.1 Analyzing large amounts of data (BDA) 10 1.6.2 Making informed decisions (IDM) 11 1.6.3 Cybersecurity (CS) 11 1.7 Big Data Analysis 11 1.7.1 Solar energy prognosis and prediction 12 1.7.2 Wind energy prognosis and prediction 12 1.7.3 Demand forecasting for electric vehicle (EV) charging stations 13 1.8 Smart Buildings and Smart Homes 13 1.8.1 Inconspicuous load monitoring and disaggregation 13 1.8.2 Modeling of load characteristics 14 1.8.3 Waste management in smart cities 14 1.8.4 Innovative methods of healthcare delivery 14 1.8.5 Evaluation of the stability of the power system 15 1.8.6 Estimation of the current state of the system 15 1.8.7 Electrical theft detection using system anomaly observation 15 1.8.8 Management of the grid's resilience 15 1.8.9 Processing of images in the digital domain 16 1.8.10 Battery monitoring and charging software 16 1.9 Conclusion 17 2 Applications of Artificial Intelligence in Intelligent Combustion and Energy Storage Technologies 27 2.1 Introduction 28 2.1.1 Artificial intelligence's role in energy storage 30 2.1.2 Development of energy storage device and the system 30 2.2 AI for the Development of Combustion Systems in Energy Vehicles 32 2.2.1 Artificial neural network 33 2.3 ANN-based Heat Transfer Prediction and Renewable Energy 34 2.3.1 AI in energy storage 36 2.3.2 Centralized control of system in AI 36 2.4 AI for Diagnostics and Numerical Tools 37 2.5 Next-Generation Energy Storage Technologies 37 2.6 Advanced Control Systems for Energy Storage 37 2.7 AI Applications in Power Sectors 38 2.8 Smart Grid with Energy Storage 39 2.9 AI in Smart Grid 39 2.10 Renewable Energy Forecast in Power Generation 40 2.11 Fault Diagnosis in Power System 40 2.12 Analysis of Consumer Energy Consumption Behavior 41 2.13 Network Security Protection in Power System 42 2.14 Conclusion 42 3 Sustainable Smart Energy Systems and Energy Preservation Strategies in Intelligent Transportation Sectors 47 3.1 Introduction 48 3.1.1 Power consumption in data centers 49 3.1.2 Advantages of ITS 51 3.2 Sustainable Smart Energy Systems and Energy Preservation Strategies in ITS 51 3.3 Communication Methods in ITS 53 3.4 Vehicles Depending on Carbon Emissions in Intelligent Transportation Systems 55 3.5 Optimized Transportation for a Sustainable Environment 56 3.6 Intelligent Traffic Management using Green Communication 57 3.7 Precaution from Pollution in Intelligent Transport Systems 59 3.8 Energy Management Techniques for the Reduction of Greenhouse Gas 61 3.9 ITS for Sustainable Mobility 62 3.10 Toward a Sustainable Ecosystem of ITS 63 3.10.1 Common system consensus and decentralization 64 3.10.2 Daemons and backward compatibility 65 3.10.3 Failure recovery and self-stabilization 65 3.10.4 Importance of sustainable development goal in transportation sectors addressing intelligent system 66 3.10.5 Renewable/green energy impact in intelligent transport and smart energy systems 66 3.11 Conclusion 67 4 Application of ANN Techniques to Mitigation of Power Quality Problems 73 4.1 Introduction 74 4.2 ANN Techniques and Analysis 76 4.2.1 Mathematical formulation of generalized FLANN 77 4.2.2 Mathematical formulation of trigonometric FLANN (T-FLANN) 78 4.2.3 Mathematical formulation of Legendre-FLANN (L-FLANN) 79 4.2.4 Mathematical formulation of recurrent NN (RNN) 81 4.2.5 Mathematical formulation for inverter switching loss computation 83 4.3 Performance and Results 83 4.3.1 Results with T-FLANN algorithm without/with PV integration 84 4.3.2 Results with L-FLANN algorithm without/with PV integration 87 4.3.3 Results with RNN algorithm without/with PV integration 90 4.4 Future Scope 93 4.5 Conclusion 94 5 Application of LMS Algorithm for Mitigation of Voltage Sag as Power Quality Problem 97 5.1 Introduction 98 5.2 Basic LMS Filtering-based Algorithm 101 5.2.1 Wiener−Hopf equations 101 5.2.2 Method of steepest descent 104 5.2.3 Least mean square algorithm 106 5.2.4 Signal-flow graph representation of the LMS algorithm 107 5.3 The Adaptive LMS Filtering-based Control of DVR 109 5.3.1 The adaptive LMS filtering-based control algorithm 109 5.3.2 Generation of unit vectors 111 5.3.3 Estimation of reference load voltages 112 5.4 Results and Performance Study 116 5.4.1 Waveform plots for the study of the dynamic performance of DVR 117 5.4.2 Error plots for the study of the dynamic performance of DVR 119 5.4.3 Behavior of the pattern of fundamental active and reactive power components 119 5.4.4 Computed data from error plots study of the steadystate performance of DVR 120 5.5 Discussion 122 5.6 Conclusion 123 6 Simple Title for Running Headers Blockchain based Solution for Electricity Supply Chain in Smart Grids 127 6.1 Introduction 128 6.1.1 Smart grid 128 6.1.2 Challenge 129 6.1.3 Electric power supply chain management 130 6.2 Related Work 132 6.3 Background of Blockchain 133 6.4 Blockchain Enabled Electricity Supply Chain 135 6.5 Conclusion 138 7 Virtual Power Plant 143 7.1 Introduction 144 7.1.1 Distributed Energy Resource (DER) 145 7.1.2 Smart grid 147 7.2 Virtual Power Plant 148 7.2.1 Components of VPP 150 7.2.2 Classification of the VPP 150 7.3 VPP System Architecture 151 7.3.1 Communication system architecture 154 7.3.2 Communication requirements 155 7.3.3 Communication technologies 156 7.3.4 Energy management system (EMS) 157 7.4 Challenges to the Implementation of the VPP 157 7.4.1 Technical challenges 157 7.4.2 Regulatory challenges 158 7.4.3 Environmental 158 7.4.4 Commercial challenges 158 7.5 Planning of the VPP 158 7.5.1 Operation of the VPP 159 7.5.2 Advantages of the VPP 159 7.6 Artificial Intelligence (AI) and the VPP 160 7.7 Case Studies on VPP 162 7.8 Conclusion 162 8 AI Business Model is Emerging Energy Market and Smart Grid 169 8.1 Introduction 170 8.2 Literature Survey 171 8.3 AI in Energy Market 172 8.3.1 Smart grid and sector coupling 172 8.3.1.1 Forecasting the power load (demand and supply prediction) 173 8.3.1.2 Grid stability 174 8.3.1.3 Fault assessment and flexible equipment 175 8.3.1.4 Security 175 8.3.1.5 Power generation forecast for renewable energy 176 8.3.1.6 Consumer consumption behavior (communication) 176 8.3.1.7 Distributed energy resource 176 8.3.2 Electricity trading 176 8.3.2.1 Financial market modeling 177 8.3.2.2 Energy trading forecast 177 8.3.2.3 Energy trading optimization 178 8.3.2.4 Blockchain 178 8.3.2.5 Smart meters 178 8.3.3 Virtual power plant 179 8.3.4 IOE 179 8.3.5 EaaS 179 8.3.6 QC 180 8.3.7 DSM 180 8.3.8 V2G 180 8.4 Summary 181 8.5 Challenges and Research Gap 182 8.6 Future Directions 183 8.7 Conclusion 183 9 Artificial Neural Network and Forecasting Major Electricity Markets 193 9.1 Introduction 194 9.2 Research Methodology 197 9.2.1 Data profile 197 9.2.2 Methods and models 197 9.2.2.1 Descriptive statistics 198 9.2.2.2 ARIMA model 198 9.2.2.3 Artificial neural networks 199 9.3 Empirical Discussion 200 9.3.1 Discussion on descriptive statistics 200 9.3.2 Discussion on plots 201 9.3.3 Discussion on the results of ARIMA predictive models 201 9.3.4 Results of stationarity:augmented Dickey−Fuller (ADF) test 202 9.3.4.1 Results of ACF and PACF 203 9.3.4.2 Results of Auto.ARIMA 203 9.3.4.3 Forecasts of the model 205 9.3.5 Discussion on the results of ANN predictive models 207 9.4 Conclusion 208 Index 215 About the Editors 217.
Weitere Ausg.:
Print version: Applications of big data and artificial intelligence in smart energy systems. Volume 2, Energy planning, operations, control and market perspectives. Gistrup : River Publishers, 2023 ISBN 9788770228275
Sprache:
Englisch
DOI:
10.1201/9781003440864
URL:
https://www.taylorfrancis.com/books/9781003440864
